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1. Reduction of Bias from Parameter Variance in Geophysical Data Estimation: Method and Application to Ice Water Content and Sedimentation Flux Estimated from Lidar

   https://doi.org/10.1175/JAS-D-19-0106.1  

   Bolot, Maximilien ; Fueglistaler, Stephan ( March 2020 , Journal of the Atmospheric Sciences)

   This paper addresses issues of statistical misrepresentation of the a priori parameters (henceforth called ancillary parameters) used in geophysical data estimation. Parameterizations using ancillary data are frequently needed to derive geophysical data of interest from remote measurements. Empirical fits to the ancillary data that do not preserve the distribution of such data may induce substantial bias. A semianalytical averaging approach based on Taylor expansion is presented to improve estimated cirrus ice water content and sedimentation flux for a range of volume extinction coefficients retrieved from spaceborne lidar observations by CALIOP combined with the estimated distribution of ancillary data from in situ aircraft measurements of ice particle microphysical parameters and temperature. It is shown that, given an idealized distribution of input parameters, the approach performs well against Monte Carlo benchmark predictions. Using examples with idealized distributions at the mean temperature for the tropics at 15 km, it is estimated that the commonly neglected variance observed in in situ measurements of effective diameters may produce a worst-case estimation bias spanning up to a factor of 2. For ice sedimentation flux, a similar variance in particle size distributions and extinctions produces a worst-case estimation bias of a factor of 9. The value of the bias is found to be mostly set by the correlation coefficient between extinction and ice effective diameter, which in this test ranged between all possible values. Systematic reporting of variances and covariances in the ancillary data and between data and observed quantities would allow for more accurate observational estimates.

    

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2. Limited iodate reduction in shipboard seawater incubations from the Eastern Tropical North Pacific oxygen deficient zone

   https://doi.org/10.1016/j.epsl.2020.116676  

   Hardisty, D.S. ; Horner, T.J. ; Evans, N. ; Moriyasu, R. ; Babbin, A.R. ; Wankel, S.D. ; Moffett, J.W. ; Nielsen, S.G. ( January 2021 , Earth and Planetary Science Letters)

   null (Ed.)
3. Revised Nitrogen Reduction Scaling Relations from Potential-Dependent Modeling of Chemical and Electrochemical Steps

   https://doi.org/10.1021/acscatal.3c01978  

   Tezak, Cooper R. ; Singstock, Nicholas R. ; Alherz, Abdulaziz W. ; Vigil-Fowler, Derek ; Sutton, Christopher A. ; Sundararaman, Ravishankar ; Musgrave, Charles B. ( October 2023 , ACS Catalysis)

   The electrochemical nitrogen reduction reaction (NRR) is a promising route to enable carbon-free ammonia production. However, this reaction is limited by the poor activity and selectivity of current catalysts. The rational design of superior NRR electrocatalysts requires a detailed mechanistic understanding of current material limitations to inform how these might be overcome. The current understanding of how scaling limits NRR on metal catalysts is predicated on a simplified reaction pathway that considers only proton-coupled electron transfer (PCET) steps. Here, we apply grand-canonical density functional theory to investigate a more comprehensive NRR mechanism that includes both electrochemical and chemical steps on 30 metal surfaces in solvent under an applied potential. We applied Φmax, a grandcanonical adaptation of the Gmax thermodynamic descriptor, to evaluate trends in catalyst activity. This approach produces a Φmax “volcano” diagram for NRR activity scaling on metals that qualitatively differs from the scaling relations identified when only PCET steps are considered. NH3* desorption was found to limit the NRR activity for materials at the top of the volcano and truncate the volcano’s peak at increasingly reducing potentials. These revised scaling relations may inform the rational design of superior NRR electrocatalysts. This approach is transferable to study other materials and reaction chemistries where both electrochemical and chemical steps are modeled under an applied potential. 

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4. Copper–Gold Interactions Enhancing Formate Production from Electrochemical CO 2 Reduction

   https://doi.org/10.1021/acscatal.9b03158  

   Tao, Zixu ; Wu, Zishan ; Yuan, Xiaolei ; Wu, Yueshen ; Wang, Hailiang ( October 2019 , ACS Catalysis)
5. Topology Optimization With Many Right-Hand Sides Using Mirror Descent Stochastic Approximation—Reduction From Many to a Single Sample

   https://doi.org/10.1115/1.4045902  

   Zhang, Xiaojia Shelly ; de Sturler, Eric ; Shapiro, Alexander ( May 2020 , Journal of Applied Mechanics)

   Abstract Practical engineering designs typically involve many load cases. For topology optimization with many deterministic load cases, a large number of linear systems of equations must be solved at each optimization step, leading to an enormous computational cost. To address this challenge, we propose a mirror descent stochastic approximation (MD-SA) framework with various step size strategies to solve topology optimization problems with many load cases. We reformulate the deterministic objective function and gradient into stochastic ones through randomization, derive the MD-SA update, and develop algorithmic strategies. The proposed MD-SA algorithm requires only low accuracy in the stochastic gradient and thus uses only a single sample per optimization step (i.e., the sample size is always one). As a result, we reduce the number of linear systems to solve per step from hundreds to one, which drastically reduces the total computational cost, while maintaining a similar design quality. For example, for one of the design problems, the total number of linear systems to solve and wall clock time are reduced by factors of 223 and 22, respectively. 

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